Modified Wiener Filter Research Articles

LSTM - Aided Speech Enhancement with Wiener Filter Adaptation

Speech enhancement plays a pivotal role in various applications, from improving the intelligibility of spoken communication in noisy environments. With the assistance of deep learning, a novel approach speech signal enhancement model is introduced in this research. The proposed LSTM model estimates the tuning factor of the Wiener filter with the aid of extracted features to obtain the de-noised speech signal. This model is structured into two phases: Training and Testing. During the training phase, Non-negative Matrix Factorization (NMF) is employed to estimate both the noise and signal spectrum from the noisy input signal. Subsequently, Empirical Mean Decomposition (EMD) features are extracted from the Wiener filter and a de-noised speech signal is obtained via processing. Additionally, bark frequency information is evaluated. In the testing phase, the LSTM model has been trained by the extracted features (EMD) via a modified wiener filter. The combination of LSTM-based temporal modeling with trained features and the adaptive Wiener filter results in significantly improved speech quality and intelligibility. Keywords— Speech Enhancement, Non-negative Matrix Factorization, Empirical Mode Decomposition, Wiener Filter.

Optimization of Median Modified Wiener Filter for Improving Lung Segmentation Performance in Low-Dose Computed Tomography Images

In low-dose computed tomography (LDCT), lung segmentation effectively improves the accuracy of lung cancer diagnosis. However, excessive noise is inevitable in LDCT, which can decrease lung segmentation accuracy. To address this problem, it is necessary to derive an optimized kernel size when using the median modified Wiener filter (MMWF) for noise reduction. Incorrect application of the kernel size can result in inadequate noise removal or blurring, degrading segmentation accuracy. Therefore, various kernel sizes of the MMWF were applied in this study, followed by region-growing-based segmentation and quantitative evaluation. In addition to evaluating the segmentation performance, we conducted a similarity assessment. Our results indicate that the greatest improvement in segmentation performance and similarity was at a kernel size 5 × 5. Compared with the noisy image, the accuracy, F1-score, intersection over union, root mean square error, and peak signal-to-noise ratio using the optimized MMWF were improved by factors of 1.38, 33.20, 64.86, 7.82, and 1.30 times, respectively. In conclusion, we have demonstrated that by applying the MMWF with an appropriate kernel size, the optimization of noise and blur reduction can enhance segmentation performance.

Lucy Richardson and Mean Modified Wiener Filter for Construction of Super-Resolution Image

The ultimate goal of the Super-Resolution (SR) technique is to generate the High-Resolution (HR) image by combining the corresponding images with Low-Resolution (LR), which is utilized for different applications such as surveillance, remote sensing, medical diagnosis, etc. The original HR image may be corrupted due to various causes such as warping, blurring, and noise addition. SR image reconstruction methods are frequently plagued by obtrusive restorative artifacts such as noise, stair casing effect, and blurring. Thus, striking a balance between smoothness and edge retention is never easy. By enhancing the visual information and autonomous machine perception, this work presented research to improve the effectiveness of SR image reconstruction The reference image is obtained from DIV2K and BSD 100 dataset, these reference LR image is converted as composed LR image using the proposed Lucy Richardson and Modified Mean Wiener (LR-MMWF) Filters. The possessed LR image is provided as input for the stage of bicubic interpolation. Afterward, the initial HR image is obtained as output from the interpolation stage which is given as input for the SR model consisting of fidelity term to decrease residual between the projected HR image and detected LR image. At last, a model based on Bilateral Total Variation (BTV) prior is utilized to improve the stability of the HR image by refining the quality of the image. The results obtained from the performance analysis show that the proposed LR-MMW filter attained better PSNR and Structural Similarity (SSIM) than the existing filters. The results obtained from the experiments show that the proposed LR-MMW filter achieved better performance and provides a higher PSNR value of 31.65dB whereas the Filter-Net and 1D,2D CNN filter achieved PSNR values of 28.95dB and 31.63dB respectively.

Ovarian cancer detection in computed tomography images using ensembled deep optimized learning classifier

SummaryOvarian cancer (OC) is one of the most common deadly diseases threatening women worldwide. In day to day life, a challenging task still exists for identifying OC in the early stages. There are different existing deep learning (DL) classification methods applied for OC detection but has some limitations: difficult to locate the exact position of the tumor and more complex. In order to overcome these problems, the proposed ensemble deep optimized classifier‐improved aquila optimization (EDOC‐IAO) classifier is introduced to detect different types of OC in computed tomography images. The image is resized and filtered in pre‐processing using the modified wiener filter (MWF). The pre‐processed image is given to the optimized ensemble classifier (ResNet, VGG‐16, and LeNet). The IAO is utilized for improving accuracy and overfitting. The fusion is done by average weighted fusion (AWF), and the image features are extracted. Finally, the softmax layer performs the OC classification and detects different ovarian tumor classes. Python is the simulation tool used. The open source TCGA‐OV dataset is used for OC classification. The proposed OC classification using the EDOC‐IAO model obtained higher accuracy (96.532%) for detecting OC than other methods.

Performance evaluation of 3D median modified Wiener filter in brain T1-weighted magnetic resonance imaging

The purpose of this study aimed to evaluate the noise reduction efficiency of a 3D median modified Wiener filter (MMWF) in brain T1-weighted magnetic resonance (MR) images. A simulation using BrainWeb phantom data and real experimental research based on data of the Alzheimer’s Disease Neuroimaging Initiative (ADNI) were performed, and the 2D MMWF was modeled to prove the usefulness of the proposed 3D MMWF. Brain MR images were obtained according to the kernel size and noise level of 2D and 3D MMWF, and the coefficient of variation (COV) and edge preservation index (EPI) were used for the quantitative evaluation of the obtained images. According to the changes in COV with respect to the changes in filter size, simulated T1-weighted images with 3D MMWF had a 2.76 times higher denoising performance than 2D MMWF. Furthermore, the EPI of simulated T1 weighted images with 3D MMWF had a 1.17 times better performance than that of simulated T1-weighted images with 2D MMWF, particularly in noisy images. To confirm the performance of 3D MMWF with clinical T1-weighted images, we obtained a T1-weighted image from ADNI and applied 2D and 3D MMWF with respect to kernel size. According to COV changes with respect to kernel size in both filters, clinical T1-weighted images showed a 1.14 times improvement with 3D MMWF and had a similar tendency as simulated images. We compared 2D and 3D MMWF in terms of tissue preservation and denoising performance in T1-weighted images. Our results indicate that the proposed 3D MMWF has better denoising performance than 2D MMWF for Rician noise and preserved the edges of brain tissues.

Speech enhancement using long short term memory with trained speech features and adaptive wiener filter.

Speech enhancement is the process of enhancing the clarity and intelligibility of speech signals that have been degraded due to background noise. With the assistance of deep learning, a novel speech signal enhancement model is introduced in this research. The proposed model is divided into two phases: (i) Training (ii) Testing. In the training phase, the noise spectrum and signal spectrum are estimated via a Non-negative Matrix Factorization (NMF) from the noisy input signal. Then, Empirical Mean Decomposition (EMD) features are extracted from the Wiener filter. The de-noised signal is acquired from EMD, the bark frequency is evaluated and the Fractional Delta AMS features are extracted. The key contribution of this study is the use of the Long Short Term Memory (LSTM) model to properly estimate the tuning factor η of the Wiener filter for all input signals. The LSTM was trained by the extracted features (EMD) via a modified wiener filter for decomposing the spectral signal and the output of EMD is the denoised enhanced speech signal. A comparative evaluation is carried out between the proposed and existing models in terms of error measures.

Fabrication and application of customized breast phantom for MMWF noise reduction algorithm in digital mammography: An experimental study

Quantitative analysis in mammography is commonly carried out using a customized breast phantom. Noise reduction algorithms such as Wiener, median, and median modified Wiener filter (MMWF) with 3×3, 5×5, 7×7, and 9×9 mask sizes are applied to digital medical images because the digital images contain noise distributions such as Gaussian noise. The purpose of this study was to validate a customized breast phantom consisting of three positions for the inner structure (Condition A: 0.5 cm; condition B: 2.0 cm, condition C: 3.0 cm from the top surface) by applying the noise reduction algorithms listed above. The acquired images were evaluated using signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and normalized noise power spectrum (NNPS) parameters. When comparing conditions A, B and C, the SNR results using condition A were 1.2 and 1.3 times better, the CNR results were 1.9 and 3.3 times better, and the NNPS results were 1.2 and 1.3 times better than for conditions B and C, respectively. The application of algorithms was useful for the reduction of noise distribution; in particular, the MMWF algorithm with a 9×9 mask size presented the best performance in terms of SNR, CNR, and NNPS results. In conclusion, we confirmed that the location of the breast lesion can affect the image quality under the same scan conditions and that noise reduction algorithms are essential processes to improve image quality.

Multi-constraints based deep learning model for automated segmentation and diagnosis of coronary artery disease in X-ray angiographic images.

BackgroundThe detection of coronary artery disease (CAD) from the X-ray coronary angiography is a crucial process which is hindered by various issues such as presence of noise, insufficient contrast of the input images along with the uncertainties caused by the motion due to respiration and variation of angles of vessels.MethodsIn this article, an Automated Segmentation and Diagnosis of Coronary Artery Disease (ASCARIS) model is proposed in order to overcome the prevailing challenges in detection of CAD from the X-ray images. Initially, the preprocessing of the input images was carried out by using the modified wiener filter for the removal of both internal and external noise pixels from the images. Then, the enhancement of contrast was carried out by utilizing the optimized maximum principal curvature to preserve the edge information thereby contributing to increasing the segmentation accuracy. Further, the binarization of enhanced images was executed by the means of OTSU thresholding. The segmentation of coronary arteries was performed by implementing the Attention-based Nested U-Net, in which the attention estimator was incorporated to overcome the difficulties caused by intersections and overlapped arteries. The increased segmentation accuracy was achieved by performing angle estimation. Finally, the VGG-16 based architecture was implemented to extract threefold features from the segmented image to perform classification of X-ray images into normal and abnormal classes.ResultsThe experimentation of the proposed ASCARIS model was carried out in the MATLAB R2020a simulation tool and the evaluation of the proposed model was compared with several existing approaches in terms of accuracy, sensitivity, specificity, revised contrast to noise ratio, mean square error, dice coefficient, Jaccard similarity, Hausdorff distance, Peak signal-to-noise ratio (PSNR), segmentation accuracy and ROC curve.DiscussionThe results obtained conclude that the proposed model outperforms the existing approaches in all the evaluation metrics thereby achieving optimized classification of CAD. The proposed method removes the large number of background artifacts and obtains a better vascular structure.

Application feasibility of median modified Wiener filter (MMWF) algorithm for customized breast ACR phantom images using 3D printing technology in mammography

The median modified Wiener filter (MMWF) algorithm, which is a combination of median and Wiener filters, was applied to reduce the noise signal contained in digital image systems. In this study, we investigated the effect of MMWF algorithm with mask sizes of 3 × 3, 5 × 5, 7 × 7, and 9 × 9 and compared it to the median and Wiener filters with a breast American College of Radiology (ACR) phantom designed using 3D printing technology. To compare the similarity between the conventional and 3D printed breast ACR phantoms at various tube voltages and tube currents, the peak signal-to-noise (PSNR) ratio was recorded. Then, the noise level of the mammographic X-ray image applied with the MMWF algorithm using various mask sizes and the conventional filtering methods were compared and evaluated. According to the results, when the MMWF algorithm using a mask size of 9 × 9 was applied to the X-ray phantom image, superior signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and coefficient of variation (COV) values were derived. In addition, by comparing the average values obtained using the conventional noise reduction methods, we confirmed that the SNR, CNR, and COV values obtained from the MMWF algorithm, using a 9 × 9 mask size, improved by 1.91, 2.29, and 4.77 times, respectively. In conclusion, applicability of the mammographic X-ray field after ACR phantom production using 3D printing technology was proven in this study. An optimal mask size of 9 × 9 for the MMWF algorithm was proposed for improving the image quality of the acquired image.

Effect of Denoising and Deblurring 18F-Fluorodeoxyglucose Positron Emission Tomography Images on a Deep Learning Model's Classification Performance for Alzheimer's Disease.

Alzheimer’s disease (AD) is the most common progressive neurodegenerative disease. 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) is widely used to predict AD using a deep learning model. However, the effects of noise and blurring on 18F-FDG PET images were not considered. The performance of a classification model trained using raw, deblurred (by the fast total variation deblurring method), or denoised (by the median modified Wiener filter) 18F-FDG PET images without or with cropping around the limbic system area using a 3D deep convolutional neural network was investigated. The classification model trained using denoised whole-brain 18F-FDG PET images achieved classification performance (0.75/0.65/0.79/0.39 for sensitivity/specificity/F1-score/Matthews correlation coefficient (MCC), respectively) higher than that with raw and deblurred 18F-FDG PET images. The classification model trained using cropped raw 18F-FDG PET images achieved higher performance (0.78/0.63/0.81/0.40 for sensitivity/specificity/F1-score/MCC) than the whole-brain 18F-FDG PET images (0.72/0.32/0.71/0.10 for sensitivity/specificity/F1-score/MCC, respectively). The 18F-FDG PET image deblurring and cropping (0.89/0.67/0.88/0.57 for sensitivity/specificity/F1-score/MCC) procedures were the most helpful for improving performance. For this model, the right middle frontal, middle temporal, insula, and hippocampus areas were the most predictive of AD using the class activation map. Our findings demonstrate that 18F-FDG PET image preprocessing and cropping improves the explainability and potential clinical applicability of deep learning models.

MRI Brain Tumor Segmentation with Intuitionist Possibilistic Fuzzy Clustering and Morphological Operations

Digital Image Processing (DIP) is a well-developed field in the biological sciences which involves classification and detection of tumour. In medical science, automatic brain tumor diagnosis is an important phase. Brain tumor detection is performed by Computer-Aided Diagnosis (CAD) systems. The human image creation is greatly achieved by an approach namely medical imaging which is exploited for medical and research purposes. Recently Automatic brain tumor detection from MRI images has become the emerging research area of medical research. Brain tumor diagnosis mainly performed for obtaining exact location, orientation and area of abnormal tissues. Cancer and edema regions inference from brain magnetic resonance imaging (MRI) information is considered to be great challenge due to brain tumors complex structure, blurred borders, besides exterior features like noise. The noise compassion is mainly reduced along with segmentation stability by suggesting efficient hybrid clustering method merged with morphological process for brain cancer segmentation. Combined form of Median Modified Wiener filter (CMMWF) is chiefly deployed for denoising, and morphological operations which in turn eliminate nonbrain tissue, efficiently dropping technique’s sensitivity to noise. The proposed system contains the main phases such as preprocessing, brain tumor extraction and post processing. Image segmentation is greatly achieved by presenting Intuitionist Possibilistic Fuzzy Clustering (IPFC) algorithm. The algorithm’s stability is greatly enhanced by this clustering along with clustering parameters sensitivity reduction. Then, the post processing of images are done through morphological operations along with Hybrid Median filtering (HMF) for attaining exact tumors representations. Additionally, suggested algorithm is substantiated by comparing with other existing segmentation algorithms. The outcomes reveal that suggested algorithm achieves improved outcomes pertaining to accuracy, sensitivity, specificity, and recall.

Integrated Denoised Synthetic Aperture Radar Images for Enhanced Digital Elevation Model Generation

Synthetic aperture radar (SAR) enables imaging of topographic surfaces day and night in different atmospheric conditions. SAR imaging systems record both intensity and phase information of the backscattered signals. Acquired intensity information is often exposed to speckle noise, and gathered phase information is usually corrupted by thermal and other types of noise. Thus, these types of noises have negative effects on interpretation of SAR images. Digital elevation model (DEM) can be generated by interferometric SAR using two SAR images, of the same area, with slightly different look angles. The generated DEM is affected by the corruption of both intensity and phase information. In this paper, a proposed framework of convolutional neural network (CNN) and modified Wiener filter (MWF) is suggested in DEM generation process. The main purpose of the proposed framework is minimizing not only speckle noise of input SAR images but also phase noise of the interferogram. Thus, an enhanced DEM can be generated. Extensive experiments are carried out and different DEMs are generated from original SAR and from both despeckled SAR images and filtered interferogram. Results and comparative analyses show significant improvements in both quality and vertical accuracy of the DEM generated by the proposed hybrid (CNN-MWF) framework.

Feasibility of MMWF noise reduction algorithm in brain SPECT images according to various reconstruction methods: A phantom study

In order to improve image quality in single-photon emission computed tomography (SPECT)/computed tomography (CT), various studies focusing on reconstruction methods and noise reduction algorithms have been conducted. Therefore, the purpose of this study was to evaluate the efficiency of the filtered back projection (FBP) and ordered subset expectation maximization (OSEM) reconstruction methods, and to evaluate image quality after applying the median modified Wiener filter (MMWF) as a complementary noise reduction filter using 3 × 3 and 5 × 5 mask sizes. The 3D Hoffman brain phantom was used, and the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were evaluated in regions of interest for quantitative analysis. Based on the results, the image quality obtained by the OSEM reconstruction method was 37.7% and 25.9% higher than that of the FBP reconstruction method regarding SNR and CNR, respectively. In addition, the average SNR and CNR values obtained by applying the MMWF algorithm were 35.9% and 17.1% higher than those without the MMWF algorithm. Therefore, our results indicate that the OSEM reconstruction method is more efficient than the FBP reconstruction method, and that the MMWF algorithm improves image quality in SPECT images, regardless of which reconstruction method is used.

Improved quality using newly designed algorithms in gamma- and X-ray fusion images with a photon counting CZT detector: Combining the median modified Wiener filter and edge detection method

In this study, we designed a photon-counting cadmium zinc telluride (CZT) gamma- and X-ray fusion imaging system and propose a new image processing method to improve image quality. The Geant4 Application for Tomographic Emission (GATE) platform was used to acquire gamma- and X-ray fusion images, and a stack-up parallel-hole collimator, which can greatly improve sensitivity, with a CZT detector was modeled. The newly proposed image processing algorithm to improve the image quality combines the median modified Wiener filter (MMWF), which can efficiently remove noise, and the Canny and Prewitt methods, which are representative edge detection algorithms. As a result, we confirmed that the two new algorithms improve noise and edge information compared to the conventional MMWF. In addition, when comparing the performance of the two proposed algorithms, we demonstrated that when the Canny is fused to MMWF, noise can be removed relatively efficiently, and the Prewitt can obtain improved edge information. The no-reference-based evaluation method was used to test these new algorithms with phantom images, with the same results. Thus, we demonstrate that the method combining MMWF and the edge detection algorithm provides superior image performance to the conventional filtering method in gamma- and X-ray fusion images with photon-counting CZT detectors.

Methods for Improving Image Quality for Contour and Textures Analysis Using New Wavelet Methods

The fidelity of an image subjected to digital processing, such as a contour/texture highlighting process or a noise reduction algorithm, can be evaluated based on two types of criteria: objective and subjective, sometimes the two types of criteria being considered together. Subjective criteria are the best tool for evaluating an image when the image obtained at the end of the processing is interpreted by man. The objective criteria are based on the difference, pixel by pixel, between the original and the reconstructed image and ensure a good approximation of the image quality perceived by a human observer. There is also the possibility that in evaluating the fidelity of a remade (reconstructed) image, the pixel-by-pixel differences will be weighted according to the sensitivity of the human visual system. The problem of improving medical images is particularly important in assisted diagnosis, with the aim of providing physicians with information as useful as possible in diagnosing diseases. Given that this information must be available in real time, we proposed a solution for reconstructing the contours in the images that uses a modified Wiener filter in the wavelet domain and a nonlinear cellular network and that is useful both to improve the contrast of its contours and to eliminate noise. In addition to the need to improve imaging, medical applications also need these applications to run in real time, and this need has been the basis for the design of the method described below, based on the modified Wiener filter and nonlinear cellular networks.

Study of the Noise Reduction Algorithm with Median Modified Wiener Filter for T2-weighted Magnetic Resonance Brain Images

The purpose of this study was to confirm the utility of the median modified Weiner filter (MMWF) noise reduction algorithm in T₂-weighted brain MR images. We acquired brain MR images using both real experiment and simulation, and performed comparative evaluation with conventional noise reduction algorithms through calculation of factors for the noise level and similarity. As a result, evaluation of the noise level and similarity showed the most improved in the image, which the MMWF noise reduction algorithm was applied. Moreover, additional experiment was conducted using real MR device and water phantom to more clearly prove the efficiency of the MMWF noise reduction algorithm. The noise level and intensity profile results derived from the real T₂-weighted MR image proved the effectiveness of the MMWF noise reduction algorithm. In conclusion, the proposed MMWF noise reduction algorithm demonstrated a very promising performance for improving the image quality in T₂-weighted MR image.

Speech enhancement using modified wiener filter based MMSE and speech presence probability estimation

In the present-day communications speech signals get contaminated due to various sorts of noises that degrade the speech quality and adversely impacts speech recognition performance. To overcome these issues, a novel approach for speech enhancement using Modified Wiener filtering is developed and power spectrum computation is applied for degraded signal to obtain the noise characteristics from a noisy spectrum. In next phase, MMSE technique is applied where Gaussian distribution of each signal i.e. original and noisy signal is analyzed. The Gaussian distribution provides spectrum estimation and spectral coefficient parameters which can be used for probabilistic model formulation. Moreover, a-priori-SNR computation is also incorporated for coefficient updation and noise presence estimation which operates similar to the conventional VAD. However, conventional VAD scheme is based on the hard threshold which is not capable to derive satisfactory performance and a soft-decision based threshold is developed for improving the performance of speech enhancement. An extensive simulation study is carried out using MATLAB simulation tool on NOIZEUS speech database and a comparative study is presented where proposed approach is proved better in comparison with existing technique.

Optimization of mask size with median modified Wiener filter algorithm for gamma images using pixelated semiconductor detector: Monte Carlo simulation study

Gamma images inherently contain noise distributions owing to the low photon count in a detector. Several methods using semiconductor detectors have been reported to overcome this challenge. Median modified Wiener filter (MMWF) algorithm is one of the effective noise reduction methods, which employs mask size as an essential parameter for optimization. In this study, we evaluated and optimized the acquired gamma image using the proposed MMWF algorithm based on the Monte Carlo simulation tool with various mask sizes (3 × 3, 5 × 5, and 7 × 7) in a cadmium telluride pixelated semiconductor detector. Our results demonstrated that the images obtained with 7 × 7 mask size showed the lowest values in the normalized noise power spectrum. The contrast-to-noise ratio and the coefficient of variation were observed to be 29.2–65.9% better for the images obtained with the 7 × 7 mask size compared with those obtained using other mask sizes. This study suggests that an excellent image quality of gamma images can be achieved with semiconductor detectors by optimizing the mask size in the MMWF algorithm.

Median modified wiener filter for improving the image quality of gamma camera images

The filter technique was applied to noise images, as noise is the significant factor that cause poor image quality due to lower photon counting. The purpose of this study is to confirm that image quality can be improved using the median modified Wiener filter (MMWF) technique; this is achieved via a National Electrical Manufacturers Association International Electrotechnical Commission body phantom with four large spheres that are filled with the 99mTc radioisotope when evaluating the image quality. Conventional filters such as Wiener, Gaussian, and median filters were designed, and signal to noise ratio, coefficient of variation, and contrast to noise ratio were used as the evaluation parameters. The improvement in the image quality was in the following order, from the least to the highest improvement, in all cases: Wiener filter, Gaussian filter, median filter, and the MMWF technique. The results show that the image quality was improved from 20.6 to 65.5%, 7.4–40.3%, and 12.7–44.7% for the SNR, COV, and CNR values, respectively, when using the MMWF technique, compared with the use of conventional filters. In conclusion, our results demonstrated that the MMWF technique is useful for reducing the noise distribution in gamma camera images.

Removal of various Noises in Dental X-ray Images using Selective Median Filter

Image processing plays major role to provide additional information in medical diagnosis. Input images contain picture information as well as noise information. Noise information is added with the images during signal acquisition stage or in the transmission of image data. Salt & Pepper noise, Gaussian noise and Speckle noise is the major noises introduced in the images. Noise information may be interpreted as data and it may lead to severe problem. Linear and Non-linear filters are used to reduce these noises in the images. In medical image analysis, non-linear filters are preferred over linear filters because it preserves edge information. Dental X-ray image is used to identify the cavities and its depth. Average filter, median filter and wiener filter are the classical techniques used in many image processing applications. In this paper, three different noises (Salt &pepper, Gaussian and Speckle noise) are added and different filters (Average filters, median filter, Wiener filter) performances are analysed with the PSNR, SNR and MSE. Analysis shows that median filter is suitable for reducing salt & pepper noise and wiener filter is suitable for reducing Gaussian noise and speckle noise in the dental x-ray images. Selective median filter is a modified wiener filter. Median filter is used for the pixel value 0 and 255.For other pixel values wiener filter is used. Selective median filter is giving better result than traditional techniques.